Detecting Secondary Peptide Structures by Scaling a Genetic Algorithm

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The ability to accurately predict a polypeptide’s molecular structure given its amino acid sequence is important to numerous scientific, medical, and engineering applications. Studies have been conducted in the application of Genetic Algorithms (GAs) to this problem with initial results shown to be promising. In this paper we use the fast messy Genetic Algorithm (fmGA) to attempt to find the minimization of an empirical CHARMM energy model and generation of the associated conformation. Previous work has shown that the fmGA provided favorable results, at least when applied to the pentapeptide [Met]-Enkephelin. We extend these results to a much larger Polyalanine peptide by utilizing secondary structure information. This information is utilized to conduct localized searches on the energy landscape. Results indicate that on average this localized search always produces a better final solution.

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Journal: TechConnect Briefs
Volume: 2, Technical Proceedings of the 2001 International Conference on Computational Nanoscience and Nanotechnology
Published: March 19, 2001
Pages: 29 - 32
Industry sectors: Advanced Materials & Manufacturing | Medical & Biotech
Topics: Biomaterials, Informatics, Modeling & Simulation
ISBN: 0-9708275-3-9